Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-0

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p> using the parameters = 5 and = 10, visualized using Java TreeView [42]. NNN clusters have been colored, internally hierarchically clustered, and the cluster centroids have in turn been hierarchically clustered to provide an easily interpretable tree

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-2

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p>and GO term basis. Each cell represents an AUC score calculated analytically using the Wilcoxon Rank Sum formula; below baseline performance appears in blue, and yellow indicates higher performance. Data set and term combinations for which ten or fewer pairs were able to be evaluated are excluded and appear as gray missing values; functions for which less than 10% of methods were available due to gene exclusion by NNN, QTC, or SAMBA were removed. Visualization provided by TIGR MeV [41]

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-3

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p>rocesses for which data was available in our analysis. For each algorithm, the maximum AUC across all six data sets was determined, and the resulting AUCs are presented here in descending order per algorithm. NNN correctly clusters genes from substantially more biological processes relative to previous methods

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods-4

<p><b>Copyright information:</b></p><p>Taken from "Nearest Neighbor Networks: clustering expression data based on gene neighborhoods"</p><p>http://www.biomedcentral.com/1471-2105/8/250</p><p>BMC Bioinformatics 2007;8():250-250.</p><p>Published online 12 Jul 2007</p><p>PMCID:PMC1941745.</p><p></p> using the parameters = 5 and = 10, visualized using Java TreeView [42]. NNN clusters have been colored, internally hierarchically clustered, and the cluster centroids have in turn been hierarchically clustered to provide an easily interpretable tree